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All Journal International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Jurnal Nasional Teknologi Komputer
Perianus Lombu
Universitas Pembangunan Panca Budi
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education

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CHANGES IN THE SOCIETY ENVIRONMENT ON THE EFFECTIVENESS OF COMPUTER NETWORK DISTRIBUTION Aldy Agustian; Ardiansyah; Perianus Lombu; Kiki Wulandari; Muhammad Amin
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 4 No. 10 (2025): SEPTEMBER
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/ijset.v5i1.1567

Abstract

The proliferation of computer networks has significantly impacted various aspects of social life. This study aims to identify and analyze the social changes resulting from the integration of computer networks into everyday life. Using a mixed-method research design, data were collected through a survey involving 300 respondents and in-depth interviews with 40 informants. The results indicate that computer networks increase the frequency of communication with family and friends (78%), facilitate access to information and education (70%), and enable more flexible work and education patterns, with 60% of respondents working from home and 50% participating in online learning. Furthermore, computer networks also increase economic opportunities such as online jobs and e-commerce businesses (68%). However, the study also reveals a digital divide, particularly in rural areas (40%), indicating the need for further efforts to ensure the equitable distribution of the benefits of technology. This study provides in-depth insights into the impact of computer networks on social transformation and can be used as a reference for policymakers, educators, and information technology professionals in developing effective strategies to leverage this technology for social and economic progress.
PENERAPAN DATA MINING DENGAN ALGORITMA K-MEANS CLUSTERING UNTUK MENGELOMPOKKAN TINGKAT KEMAMPUAN AKADEMIK SISWA DI SMK NEGERI 2 ULU MOROO Perianus Lombu
Jurnal Nasional Teknologi Komputer Vol 6 No 3 (2026): Juli 2026
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The utilization of academic data in educational institutions is often underutilized, leading to the "Data Rich, Information Poor" phenomenon. At SMK Negeri 2 Ulu Moroo, student ability evaluation still relies on a single linear average value, which contains methodological flaws as it disguises the disparity between theoretical competence (cognitive) and practical vocational skills (psychomotor). To address this issue, this research applies Educational Data Mining (EDM) techniques using the K-Means Clustering algorithm based on the CRISP-DM framework. The study involved $N = 176$ students from grades X and XI, with feature variables including Cognitive ($X_1$), Psychomotor ($X_2$), and Affective ($X_3$) scores, which were homogenized using Min-Max Normalization $[0, 1]$ to eliminate scale bias. Cluster validation using a combination of the Elbow Method and Silhouette Coefficient determined the optimal number of clusters at $k = 3$, with a WCSS variance reduction of $59.95\%$ and Silhouette score of $0.68$ (Strong Structure). Centroid denormalization partitioned student academic profiles into three categories, namely Cluster 1 or High Achievers consisting of 62 students showing linear dominance with cognitive score of $86.45$ and a psychomotor score of $89.20$, Cluster 2 or Middle Achievers with 79 students having a stable cognitive score of $74.20$ but a fluctuating psychomotor score of $78.10$, and Cluster 3 or Underachievers comprising 35 students performing below the passing grade with a cognitive score of $68.70$ and a psychomotor score of $66.40$. These findings serve as a Decision Support System the school to implement differentiated learning, targeted remedial programs, and evidence-based industrial internship placements.
Co-Authors Aldy Agustian Amin, Muhammad Ardiansyah Kiki Wulandari